Hand Mesh Reconstruction
Hand Mesh Reconstruction - Related Videos
08:18
Three-Dimensional Reconstruction of Orbital Fractures
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Cited by 2 •
2025
Personalized medicine for orbital reconstruction is developing rapidly. Due to the delicate nature of the orbit, small discrepancies following fracture reconstruction may cause impairment in visual perception. Here, we describe three methods for virtual 3D reconstruction of orbital defects and their indications and potential pitfalls for correct reconstruction.
09:41
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Cited by 6 •
2023
When we grasp an object, multiple regions of the fingers and hand typically make contact with the object's surface. Reconstructing such contact regions is challenging. Here, we present a method for approximately estimating the contact regions by combining marker-based motion capture with existing deep learning-based hand mesh reconstruction.
09:35
Measurement of the Hand Transmitted Vibration of the Human Hand Arm System During Operation of a Hand Tractor
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Cited by 1 •
2021
Here, we present a standardized method for measurement of the hand transmitted vibration from handles of a single-axle tractor with special reference to changes in grip force and vibration frequency.
01:20
Mesh Analysis
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2024
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved. A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
10:32
Transvaginal Mesh Insertion in the Ovine Model
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Cited by 11 •
2017
This protocol describes mesh implantation in the ovine rectovaginal septum using a single vaginal incision technique, with and without the trocar-guided insertion of anchoring arms.
FAQs
Learned models infer several linked elements from images or video: hand landmarks, joint configuration, surface vertices, and finger articulation. These estimates are then organized into a connected mesh, allowing the system to represent both the hand’s outer surface and its articulated structure. This combined representation supports analysis of changes in hand shape and movement rather than isolated point detection.
Each element captures a different aspect of hand behavior. Landmarks provide identifiable reference points, joint configuration describes the arrangement of articulated parts, and surface vertices represent the hand’s detailed shape. Considering these components together produces a richer representation than tracking a single feature type, which is important when studying coordination, manipulation, or gesture-related changes.
A single reconstructed hand state describes shape and pose at one moment, whereas a sequence reveals how those properties change during movement. Temporal information therefore helps characterize finger articulation, coordination, and manipulation as behaviors unfold. It also supports motion tracking and gesture recognition by preserving the progression from one hand configuration to the next.
The workflow can begin with images or video as visual input. A learned model processes that material to estimate landmarks, joint configuration, surface vertices, and finger articulation, after which the elements are organized into a connected mesh. The resulting representation can then be examined for hand shape, pose, and movement patterns relevant to behavioral analysis.
In behavior research, the reconstructed representation provides quantitative information about coordination, manipulation, and nonverbal communication. Researchers can examine how hand shape and movement vary across observed actions, rather than relying only on qualitative descriptions. This makes the technique relevant to studies of hand behavior in contexts where gestures, object-related movements, or communicative signals are important.
The representation supports gesture recognition, motion tracking, and human-computer interaction, where systems must interpret or respond to hand movement. It also has relevance in robotics and biomechanics, which can use information about articulation and coordination, as well as interactive systems that depend on changes in hand pose or shape over time.